Philip O'Neill: Research Interests
Statistical inference for infectious disease data
I am interested in the development of new methodology for analysing data from outbreaks
of communicable diseases, and also applying the methods to data from studies. Much of my
interest on the methodology side has been around Bayesian Markov chain Monte Carlo (MCMC) methods,
and in particular data augmentation techniques. Application areas have so far included
influenza, norovirus, measles, E. Coli and some other pathogens.
Healthcare associated infections
Some of the above methods have recently been fruitfully applied to data taken from detailed
hospital studies concerned with nosocomial pathogens such as Methicillin Resistant
Staphylococcus Aureus (MRSA). In particular this approach is more powerful than that
provided by conventional statistical methods both in terms of what is actually assumed,
and what can be estimated.
Stochastic epidemic models
I am interested in analysis of stochastic epidemic models, in particular branching
process approximations, coupling methods, and quasistationarity.
Other areas of general interest include population modelling, mathematical biology (e.g.
cell-level modelling), and quasistationarity of Markov Processes.
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